Research on the model of piano practice time allocation and performance effect evaluation based on integer programming
Bibliographic record
Abstract
In this paper, the problem of piano practice time allocation is categorized as an integer planning problem, and focuses on 0-1 integer planning in integer planning.Based on the advantageous information in the 0-1 integer programming problem, the value of feasible solutions and the index set corresponding to the feasible solutions are proposed to realize the piano practice time allocation based on integer programming.For the evaluation of piano playing effect, a piano playing effect evaluation method based on the extraction of musical melody features is proposed, which adopts the base note cycle extraction algorithm based on the short-time autocorrelation method to extract the base note of the musical melody, and improves the linear scaling algorithm to solve the problem of uneven playing speeds and so on.In the piano practice practice allocation experiment, the average allocation time of player A applying the time allocation method of this paper is 2516s, which is higher than that of player B with the traditional allocation time, and the average concentration time accounts for 98.53% of the average time, which is better than that of player B's 95.43% share.Compared with the traditional manual evaluation method, the evaluation results of this paper's piano playing effect evaluation method in different test times sum up to 1, and the evaluation effect is better.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".